libraries
library(tidyverse)
package ‘tidyverse’ was built under R version 4.0.2Registered S3 methods overwritten by 'dbplyr':
method from
print.tbl_lazy
print.tbl_sql
[30m── [1mAttaching packages[22m ──────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──[39m
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package ‘ggplot2’ was built under R version 4.0.2package ‘tibble’ was built under R version 4.0.2package ‘tidyr’ was built under R version 4.0.2package ‘readr’ was built under R version 4.0.2package ‘dplyr’ was built under R version 4.0.2package ‘forcats’ was built under R version 4.0.2[30m── [1mConflicts[22m ─────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(ggplot2)
library(dbplyr)
package ‘dbplyr’ was built under R version 4.0.2
Attaching package: ‘dbplyr’
The following objects are masked from ‘package:dplyr’:
ident, sql
library(lmerTest)
package ‘lmerTest’ was built under R version 4.0.2Loading required package: lme4
package ‘lme4’ was built under R version 4.0.2Loading required package: Matrix
Attaching package: ‘Matrix’
The following objects are masked from ‘package:tidyr’:
expand, pack, unpack
Attaching package: ‘lmerTest’
The following object is masked from ‘package:lme4’:
lmer
The following object is masked from ‘package:stats’:
step
library(languageR)
package ‘languageR’ was built under R version 4.0.2
library(lme4)
library(wesanderson)
package ‘wesanderson’ was built under R version 4.0.2
cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "D55E00", "#CC79A7")
source("helpers.r")
package ‘bootstrap’ was built under R version 4.0.2
data filtering
data = read.csv("main-trials.csv")
demo = read.csv("main-subject_information.csv")
data <- data %>%
left_join(demo, by="workerid")
data = data %>%
filter(attention_response != "NA" & item_type != "example3") %>%
mutate(attention_pass = ifelse(attention_response == attention_correct_response, 1, 0)) %>%
arrange(workerid)
attention_exclusion = data %>%
group_by(workerid) %>%
summarize(accuracy = mean(attention_pass)) %>%
mutate(exclude = ifelse(accuracy < 0.8, "yes", "no")) %>%
filter(exclude == "yes")
attention_correct = data %>%
group_by(workerid) %>%
summarize(accuracy = mean(attention_pass)) %>%
mutate(exclude = ifelse(accuracy < 0.8, "yes", "no")) %>%
filter(exclude == "no")
nonnative = demo %>%
filter(native != "yes")
experiment completion times before exclusions
data %>%
group_by(workerid) %>%
summarize(time = mean(Answer.time_in_minutes)) %>%
ggplot(aes(x=time)) +
geom_histogram() +
xlab("Average completion time") +
ylab("Number of cases") +
theme_bw()

Excluding from data frames
data = data[!(data$workerid %in% attention_exclusion$workerid),]
data = data[!(data$workerid %in% nonnative$workerid),]
demo = demo[!(demo$workerid %in% attention_exclusion$workerid),]
demo = demo[!(demo$workerid %in% nonnative$workerid),]
Time exclusions after filtering data based on attention checks and native language
time_exclusions = data %>%
group_by(workerid) %>%
summarize(time = mean(Answer.time_in_minutes)) %>%
filter(time < 10)
length(unique(time_exclusions$workerid))
[1] 9
experiment completion times before time exclusions
data %>%
group_by(workerid) %>%
summarize(time = mean(Answer.time_in_minutes)) %>%
ggplot(aes(x=time)) +
geom_histogram() +
xlab("Average completion time") +
ylab("Number of cases") +
theme_bw()

data = data[!(data$workerid %in% time_exclusions$workerid),]
demo = demo[!(demo$workerid %in% time_exclusions$workerid),]
double check demo and data are the same
length(demo$workerid)
[1] 131
length(unique(data$workerid))
[1] 131
write.csv(demo, "demo.csv")
change character variable to factor variables
data = data %>%
mutate(age = as.numeric(age)) %>%
mutate_if(is.character,as.factor)
NAs introduced by coercion
demo = demo %>%
mutate(age = as.numeric(age))
NAs introduced by coercion
demo = demo %>%
mutate_if(is.character,as.factor)
Main data visualizations
Set the background to be white instead of gray.
theme_set(theme_bw())
experiment completion times
data %>%
group_by(workerid) %>%
summarize(time = mean(Answer.time_in_minutes)) %>%
ggplot(aes(x=time)) +
geom_histogram() +
xlab("Average completion time") +
ylab("Number of cases") +
theme_bw()

Filtering data by critical and filler trials. droplevels() gets rid of unused factor levels (i.e. “filler” in data_critical)
data_critical = data %>%
filter(trial_type == "critical") %>%
droplevels()
data_filler = data %>%
filter(trial_type == "filler")
data_critical %>%
group_by(context_condition, DA_presence) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=reorder(context_condition, mean_felicity), y=mean_felicity, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
#geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_manual(values = cbPalette) +
scale_fill_manual(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
geom_text(aes(label = mean_felicity), size = 3) +
theme(axis.text.x = element_text(vjust = 0.7))
`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing
scale.

ggsave(file = "graphs/felicity_barplot.png",width=5,height=4)
data_critical %>%
group_by(DA_presence, context_condition) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=DA_presence, y=mean_felicity, fill = context_condition)) +
geom_bar(stat = "identity", position = dodge) +
#geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_manual(values = cbPalette) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
#geom_text(aes(label = mean_felicity), size = 3) +
theme(axis.text.x = element_text(vjust = 0.7))
`summarise()` has grouped output by 'DA_presence'. You can override using the `.groups` argument.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing
scale.


data_critical %>%
group_by(context_condition, DA_presence) %>%
ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA")) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

data_critical %>%
group_by(context_condition, DA_presence) %>%
summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>%
ggplot(aes(x=reorder(context_condition, mean_affect), y=mean_affect, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
#geom_jitter(data=data_critical,aes(y=affect_rating),alpha=.5,color="lightblue") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
labs (x="Context Condition", y= "Mean Affect Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
#scale_fill_manual(values = cbPalette) +
scale_fill_manual(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
geom_text(aes(label = mean_affect), size = 3) +
theme(axis.text.x = element_text(vjust = 0.7))
`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

ggsave(file = "graphs/affect_barplot.png",width=5,height=4)
data_critical %>%
group_by(context_condition, DA_presence) %>%
ggplot(aes(x = context_condition,y=affect_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA")) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.


dodge = position_dodge(.9)
data %>%
group_by(context_condition, DA_presence) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#scale_fill_manual(values = cbPalette) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7))
`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

dodge = position_dodge(.9)
data %>%
group_by(context_condition, DA_presence) %>%
summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#scale_fill_manual(values = cbPalette) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7))
`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

data %>%
group_by(condition) %>%
summarize(mean_affect = mean(affect_rating)) %>%
ggplot(aes(x=reorder(condition,mean_affect), y=mean_affect)) +
geom_bar(stat = "identity") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.7))

data_critical %>%
ggplot(aes(x = affect_rating, y = felicity_rating, color = condition)) +
geom_point() +
geom_smooth(method="lm") +
theme_bw()

data %>%
group_by(condition) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = condition)) +
geom_point() +
theme_bw()

data_critical %>%
group_by(context_condition, DA_presence) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition)) +
geom_point(size =3) +
theme_bw()
`summarise()` has grouped output by 'context_condition'. You can override using the `.groups` argument.

data_critical %>%
group_by(context_condition, DA_presence, item_type) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, group = DA_presence)) +
geom_point(size =3) +
geom_smooth(method = "lm") +
theme_bw()
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

Variation by item types
data_critical %>%
group_by(context_condition, DA_presence, item_type) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, group = DA_presence, label = item_type)) +
geom_point(size =3) +
geom_text() +
geom_smooth(method = "lm")
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
group_by(context_condition, DA_presence, item_type) %>%
ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA")) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
facet_wrap(~item_type)
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

data_critical %>%
group_by(context_condition, DA_presence, item_type) %>%
ggplot(aes(x = context_condition,y=affect_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA")) +
facet_wrap(~item_type)

data_critical %>%
group_by(DA_presence, item_type) %>%
ggplot(aes(x = item_type,y=felicity_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence") +
#scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA"))

data_critical %>%
group_by(DA_presence, item_type) %>%
ggplot(aes(x = item_type,y=affect_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Item Type", y= "Affect Rating", fill = "DA Presence") +
#scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA"))

Plot the felcity and affect ratings for each item_type by using facets.
ggplot(data, aes(x=affect_rating,y=felicity_rating, color=condition)) +
geom_point() +
geom_smooth(method="lm") +
facet_wrap(~item_type)

Plot the felcity and affect ratings for each item_type by using facets.
ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=condition)) +
geom_point() +
#geom_smooth(method="lm") +
geom_smooth() +
facet_wrap(~item_type)

ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=context_condition)) +
geom_point() +
geom_smooth(method="lm") +
facet_wrap(~item_type)

ggplot(data_filler, aes(x=affect_rating,y=felicity_rating, color=context_condition)) +
geom_point() +
geom_smooth(method="lm") +
facet_wrap(~item_type)



ggsave(file = "graphs/DA_by_item_type.png",width=10,height=7)
ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
geom_point() +
geom_smooth(method="lm") +
#geom_smooth() +
labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
#scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
scale_color_manual(values = cbPalette) +
facet_wrap(~referent_gender)

ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
geom_point() +
geom_smooth(method="lm") +
#geom_smooth() +
labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
#scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
scale_color_manual(values = cbPalette) +
facet_grid(rows = vars(item_type), cols = vars(referent_gender))

dodge = position_dodge(.9)
data_critical %>%
group_by(referent_gender, DA_presence, item_type) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=referent_gender, y=mean_felicity, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#scale_fill_manual(values = cbPalette) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7)) +
facet_wrap(~item_type)
`summarise()` has grouped output by 'referent_gender', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
group_by(DA_presence, referent_gender) %>%
ggplot(aes(x = referent_gender,y=felicity_rating, fill = DA_presence)) +
geom_boxplot() +
#labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
#scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA")) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

data_critical %>%
group_by(DA_presence, referent_gender) %>%
ggplot(aes(x = referent_gender,y=affect_rating, fill = DA_presence)) +
geom_boxplot() +
#labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
#scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_discrete(labels=c("DA", "no DA")) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

dodge = position_dodge(.9)
data_critical %>%
group_by(context_condition, DA_presence, item_type) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#scale_fill_manual(values = cbPalette) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7)) +
facet_wrap(~item_type)
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

dodge = position_dodge(.9)
data_critical %>%
group_by(context_condition, DA_presence, item_type) %>%
summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#scale_fill_manual(values = cbPalette) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7)) +
facet_wrap(~item_type)
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

Exploratory data visualizations for social variables
data %>%
group_by(region_country) %>%
ggplot(aes(x = fct_infreq(region_country), fill=region_country)) +
geom_bar(stat = 'count') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.7)) +
scale_fill_manual(values = wes_palette("Zissou1", 15, type = "continuous")) +
theme(legend.position = "none")

demo %>%
group_by(region_country) %>%
ggplot(aes(x = fct_infreq(region_country), fill=region_country)) +
geom_bar(stat = 'count') +
theme(axis.text.x = element_text(angle = 45, vjust = 0.7)) +
scale_fill_manual(values = wes_palette("Zissou1", 15, type = "continuous")) +
theme(legend.position = "none") +
labs (x="Country", y= "Number of Participants")

ggsave(file = "graphs/participants_country.png",width=5,height=4)
dodge = position_dodge(.9)
data_critical %>%
group_by(context_condition, DA_presence, region_country) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#scale_fill_manual(values = cbPalette) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7)) +
facet_wrap(~region_country)
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
group_by(context_condition, DA_presence, region_country) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
geom_point() +
facet_wrap(~region_country) +
theme_bw()
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
group_by(context_condition, DA_presence, region_country) %>%
filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
#filter(trial_sum > 25) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
geom_point() +
facet_wrap(~region_country) +
theme_bw()
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
group_by(context_condition, DA_presence, region_country) %>%
ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "CG", "neutralNoCG" = "No CG")) +
scale_fill_discrete(labels=c("DA", "no DA")) +
facet_wrap(~region_country)

data_critical %>%
group_by(context_condition, DA_presence, region_country) %>%
filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#scale_fill_manual(values = cbPalette) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7)) +
facet_wrap(~region_country)
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
group_by(DA_presence, region_country) %>%
ggplot(aes(x = region_country,y=felicity_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Country", y= "Felicity Rating", fill = "DA Presence") +
scale_fill_discrete(labels=c("DA", "no DA")) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

ggsave(file = "graphs/country_felicity_boxplot.png",width=7,height=5)
data_critical %>%
filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
group_by(condition, region_country) %>%
ggplot(aes(x = region_country,y=felicity_rating, fill = condition)) +
geom_boxplot() +
labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence")

#scale_fill_discrete(labels=c("DA", "no DA"))
data_critical %>%
filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
group_by(DA_presence, region_country) %>%
ggplot(aes(x = region_country,y=affect_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Country", y= "Affect Rating", fill = "DA Presence") +
scale_fill_discrete(labels=c("DA", "no DA")) +
scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

ggsave(file = "graphs/country_affect_boxplot.png",width=7,height=5)
data_critical %>%
group_by(context_condition, DA_presence, education) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
geom_point() +
facet_wrap(~education) +
theme_bw()
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
filter(!is.na(age)) %>%
group_by(workerid, age, DA_presence) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
ggplot(aes(x = age, y = mean_felicity, color = DA_presence)) +
geom_point(size =3) +
geom_smooth(method = "lm") +
theme_bw()
`summarise()` has grouped output by 'workerid', 'age'. You can override using the `.groups` argument.

data_critical %>%
filter(!is.na(age)) %>%
filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
group_by(workerid, age) %>%
summarize(reported_usage = paste(unique(reported_usage))) %>%
ggplot(aes(x = age, y = as.numeric(reported_usage))) +
geom_point(size =3) +
geom_smooth(method = "lm") +
theme_bw()
`summarise()` has grouped output by 'workerid'. You can override using the `.groups` argument.

data_critical %>%
filter(!is.na(age)) %>%
group_by(workerid, age, DA_presence) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
ggplot(aes(x = age, y = mean_affect, color = DA_presence)) +
geom_point(size =3) +
geom_smooth(method = "lm") +
geom_hline(yintercept = 0.5) +
theme_bw()
`summarise()` has grouped output by 'workerid', 'age'. You can override using the `.groups` argument.

data_critical %>%
group_by(context_condition, DA_presence, reported_usage) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
geom_point() +
facet_wrap(~reported_usage)
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.



data_critical %>%
filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
group_by(condition, reported_usage) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x= reorder(reported_usage, mean_felicity), y=mean_felicity, fill = condition)) +
geom_bar(stat = "identity", position = dodge) +
#geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
#labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
#scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_manual(values = cbPalette) +
#scale_fill_manual(values = wes_palette("FantasticFox1")) +
theme_bw() +
theme(axis.text.x = element_text(vjust = 0.7))
`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

dodge = position_dodge(.9)
data_critical %>%
filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
group_by(DA_presence, reported_usage, context_condition) %>%
summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_manual(values = cbPalette) +
#scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
theme(axis.text.x = element_text(vjust = 0.7)) +
facet_wrap(~ reported_usage)
`summarise()` has grouped output by 'DA_presence', 'reported_usage'. You can override using the `.groups` argument.

dodge = position_dodge(.9)
data_critical %>%
filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
group_by(DA_presence, reported_usage, context_condition) %>%
summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>%
ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) +
geom_bar(stat = "identity", position = dodge) +
#geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
scale_fill_manual(values = cbPalette) +
#scale_fill_manual(values = wes_palette("FantasticFox1")) +
theme(axis.text.x = element_text(vjust = 0.7)) +
facet_wrap(~ reported_usage)
`summarise()` has grouped output by 'DA_presence', 'reported_usage'. You can override using the `.groups` argument.


data_critical %>%
group_by(context_condition, DA_presence, reported_familiarity) %>%
summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
geom_point() +
facet_wrap(~reported_familiarity)
`summarise()` has grouped output by 'context_condition', 'DA_presence'. You can override using the `.groups` argument.

data_critical %>%
#filter(reported_familiarity == "0" | reported_familiarity == "1" | reported_familiarity == "2" | reported_familiarity == "3" ) %>%
group_by(DA_presence, reported_familiarity) %>%
ggplot(aes(x = reported_familiarity,y=felicity_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence") +
scale_fill_discrete(labels=c("DA", "no DA"))

data_critical %>%
#filter(reported_familiarity == "0" | reported_familiarity == "1" | reported_familiarity == "2" | reported_familiarity == "3") %>%
group_by(DA_presence, reported_familiarity) %>%
ggplot(aes(x = reported_familiarity,y=affect_rating, fill = DA_presence)) +
geom_boxplot() +
labs (x="Item Type", y= "Affect Rating", fill = "DA Presence") +
scale_fill_discrete(labels=c("DA", "no DA"))

Data modeling
Releveling (changing reference level) and centering DA predictor and context condition predictor
data_model = data_critical %>%
mutate(context_condition = fct_relevel(context_condition, "neutralNoCG")) %>%
mutate(numDA_presence = as.numeric(DA_presence), numcontext_condition = as.numeric(context_condition)) %>%
mutate(cDA_presence = scale(numDA_presence), cContext_condition = scale(numcontext_condition))
- (OSF preregistered analysis) Linear mixed effects analysis, predicting felicity from a centered presence of the DA predictor and a centered context condition predictor. By subject and by item, random intercepts as well as random slopes for condition and DA presence.
felicity_mdl <- lmer(felicity_rating ~ cDA_presence*cContext_condition + (1 + cDA_presence + cContext_condition|workerid) + (1 + cDA_presence + cContext_condition|item_type), data=data_model, REML=F)
summary(felicity_mdl)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ cDA_presence * cContext_condition + (1 + cDA_presence +
cContext_condition | workerid) + (1 + cDA_presence + cContext_condition | item_type)
Data: data_model
AIC BIC logLik deviance df.resid
329.1 413.3 -147.5 295.1 1031
Scaled residuals:
Min 1Q Median 3Q Max
-2.9840 -0.5603 0.1028 0.5781 2.9673
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.022473 0.14991
cDA_presence 0.011299 0.10630 -0.68
cContext_condition 0.004487 0.06699 -0.27 -0.35
item_type (Intercept) 0.001935 0.04398
cDA_presence 0.000970 0.03115 0.26
cContext_condition 0.001135 0.03369 -0.77 0.25
Residual 0.055257 0.23507
Number of obs: 1048, groups: workerid, 131; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.62679 0.02168 18.34441 28.913 < 2e-16 ***
cDA_presence 0.10514 0.01629 16.18323 6.456 7.5e-06 ***
cContext_condition 0.07247 0.01531 10.20344 4.734 0.000757 ***
cDA_presence:cContext_condition 0.02223 0.00777 858.01487 2.862 0.004317 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cDA_pr cCntx_
cDA_presenc -0.106
cCntxt_cndt -0.494 0.059
cDA_prsn:C_ -0.002 -0.018 -0.002
Simple regressions to analyze interaction in felicity_mdl
summary(felicity_mdl.simple)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ DA_presence * context_condition - context_condition +
(1 + DA_presence + context_condition | workerid) + (1 + DA_presence +
context_condition | item_type)
Data: data_model
AIC BIC logLik deviance df.resid
329.1 413.3 -147.5 295.1 1031
Scaled residuals:
Min 1Q Median 3Q Max
-2.9840 -0.5603 0.1028 0.5780 2.9672
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.051928 0.22788
DA_presencenoDA 0.048676 0.22063 -0.69
context_conditionneutralCG 0.017948 0.13397 -0.36 -0.35
item_type (Intercept) 0.005925 0.07697
DA_presencenoDA 0.004175 0.06462 -0.27
context_conditionneutralCG 0.004544 0.06741 -0.97 0.25
Residual 0.055257 0.23507
Number of obs: 1048, groups: workerid, 131; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.48972 0.03646 17.85773 13.430 9.01e-11 ***
DA_presencenoDA 0.17067 0.03792 25.57704 4.500 0.00013 ***
DA_presenceDA:context_conditionneutralCG 0.11122 0.03284 13.47242 3.386 0.00465 **
DA_presencenoDA:context_conditionneutralCG 0.20352 0.03680 21.11904 5.531 1.70e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DA_pDA DA_prsncDA:c_CG
DA_prsncnDA -0.459
DA_prsncDA:c_CG -0.759 0.213
DA_prsncnDA:_CG -0.526 -0.208 0.576
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00230512 (tol = 0.002, component 1)
summary(felicity_mdl.simple1.2)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ DA_presence * context_condition - context_condition +
(1 + DA_presence + context_condition | workerid) + (1 | item_type)
Data: data_model
AIC BIC logLik deviance df.resid
339.9 399.4 -158.0 315.9 1036
Scaled residuals:
Min 1Q Median 3Q Max
-3.1959 -0.5365 0.1418 0.5975 3.0964
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.052982 0.23018
DA_presencenoDA 0.048529 0.22029 -0.69
context_conditionneutralCG 0.016746 0.12941 -0.35 -0.38
item_type (Intercept) 0.002071 0.04551
Residual 0.057499 0.23979
Number of obs: 1048, groups: workerid, 131; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.48721 0.02934 48.65604 16.607 < 2e-16 ***
DA_presencenoDA 0.17455 0.03041 264.31048 5.740 2.59e-08 ***
DA_presenceDA:context_conditionneutralCG 0.11187 0.02267 238.40614 4.935 1.51e-06 ***
DA_presencenoDA:context_conditionneutralCG 0.20006 0.02806 450.44717 7.130 4.02e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DA_pDA DA_prsncDA:c_CG
DA_prsncnDA -0.526
DA_prsncDA:c_CG -0.424 0.184
DA_prsncnDA:_CG -0.092 -0.511 0.191
felicity_mdl.simple2 <- lmer(felicity_rating ~ context_condition*DA_presence - DA_presence + (1 + DA_presence + context_condition|workerid) + (1 + DA_presence + context_condition|item_type), data=data_model, REML=F)
summary(felicity_mdl.simple2)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ context_condition * DA_presence - DA_presence +
(1 + DA_presence + context_condition | workerid) + (1 + DA_presence + context_condition | item_type)
Data: data_model
AIC BIC logLik deviance df.resid
329.1 413.3 -147.5 295.1 1031
Scaled residuals:
Min 1Q Median 3Q Max
-2.9838 -0.5604 0.1028 0.5781 2.9673
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.051932 0.22789
DA_presencenoDA 0.048681 0.22064 -0.69
context_conditionneutralCG 0.017942 0.13395 -0.36 -0.35
item_type (Intercept) 0.005926 0.07698
DA_presencenoDA 0.004182 0.06467 -0.27
context_conditionneutralCG 0.004543 0.06740 -0.97 0.26
Residual 0.055258 0.23507
Number of obs: 1048, groups: workerid, 131; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.48972 0.03647 17.85360 13.429 9.05e-11 ***
context_conditionneutralCG 0.11122 0.03284 13.47111 3.386 0.004651 **
context_conditionneutralNoCG:DA_presencenoDA 0.17067 0.03793 25.51918 4.499 0.000131 ***
context_conditionneutralCG:DA_presencenoDA 0.26297 0.03699 23.06763 7.109 3.01e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cnt_CG c_NCG:
cntxt_cndCG -0.759
c_NCG:DA_DA -0.459 0.213
cn_CG:DA_DA -0.321 -0.096 0.630
- Linear mixed effects analysis, predicting affect from a centered presence of DA predictor and and a centered context condition predictor. By subject and by item, random intercepts as well as random slopes for condition and DA presence.
affect_mdl <- lmer(affect_rating ~ cDA_presence*cContext_condition + (1 + cDA_presence + cContext_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: affect_rating ~ cDA_presence * cContext_condition + (1 + cDA_presence +
cContext_condition | workerid) + (1 | item_type)
Data: data_model
AIC BIC logLik deviance df.resid
-616.3 -556.9 320.2 -640.3 1036
Scaled residuals:
Min 1Q Median 3Q Max
-3.9958 -0.5019 0.0226 0.5831 2.9014
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.0170904 0.13073
cDA_presence 0.0031298 0.05594 -0.34
cContext_condition 0.0005461 0.02337 0.25 -0.27
item_type (Intercept) 0.0011279 0.03358
Residual 0.0220981 0.14865
Number of obs: 1048, groups: workerid, 131; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.664e-01 1.713e-02 2.370e+01 38.896 < 2e-16 ***
cDA_presence 2.637e-02 6.859e-03 1.306e+02 3.845 0.000187 ***
cContext_condition 1.755e-02 5.255e-03 1.337e+02 3.340 0.001086 **
cDA_presence:cContext_condition 4.261e-03 4.887e-03 8.595e+02 0.872 0.383523
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cDA_pr cCntx_
cDA_presenc -0.160
cCntxt_cndt 0.065 -0.065
cDA_prsn:C_ 0.002 0.001 0.010
affect_mdl.simple <- lmer(affect_rating ~ DA_presence*context_condition - context_condition + (1 + DA_presence + context_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl.simple)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: affect_rating ~ DA_presence * context_condition - context_condition +
(1 + DA_presence + context_condition | workerid) + (1 | item_type)
Data: data_model
AIC BIC logLik deviance df.resid
-616.3 -556.9 320.2 -640.3 1036
Scaled residuals:
Min 1Q Median 3Q Max
-3.9958 -0.5019 0.0226 0.5831 2.9014
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.021142 0.14540
DA_presencenoDA 0.013485 0.11612 -0.56
context_conditionneutralCG 0.002185 0.04674 0.14 -0.27
item_type (Intercept) 0.001128 0.03359
Residual 0.022098 0.14865
Number of obs: 1048, groups: workerid, 131; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.63168 0.01942 37.13365 32.528 < 2e-16 ***
DA_presencenoDA 0.04562 0.01765 281.54763 2.585 0.01024 *
DA_presenceDA:context_conditionneutralCG 0.02864 0.01280 258.42126 2.237 0.02612 *
DA_presencenoDA:context_conditionneutralCG 0.04633 0.01670 519.65179 2.774 0.00574 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) DA_pDA DA_prsncDA:c_CG
DA_prsncnDA -0.431
DA_prsncDA:c_CG -0.279 0.295
DA_prsncnDA:_CG 0.024 -0.492 0.073
affect_mdl.simple2 <- lmer(affect_rating ~ context_condition*DA_presence - DA_presence + (1 + DA_presence + context_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl.simple2)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: affect_rating ~ context_condition * DA_presence - DA_presence +
(1 + DA_presence + context_condition | workerid) + (1 | item_type)
Data: data_model
AIC BIC logLik deviance df.resid
-616.3 -556.9 320.2 -640.3 1036
Scaled residuals:
Min 1Q Median 3Q Max
-3.9958 -0.5019 0.0226 0.5831 2.9014
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.021142 0.14540
DA_presencenoDA 0.013484 0.11612 -0.56
context_conditionneutralCG 0.002185 0.04674 0.14 -0.27
item_type (Intercept) 0.001128 0.03359
Residual 0.022098 0.14865
Number of obs: 1048, groups: workerid, 131; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.63168 0.01942 37.14235 32.529 < 2e-16 ***
context_conditionneutralCG 0.02864 0.01280 258.43091 2.237 0.026123 *
context_conditionneutralNoCG:DA_presencenoDA 0.04562 0.01765 281.55716 2.585 0.010241 *
context_conditionneutralCG:DA_presencenoDA 0.06331 0.01731 259.06531 3.657 0.000309 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cnt_CG c_NCG:
cntxt_cndCG -0.279
c_NCG:DA_DA -0.431 0.295
cn_CG:DA_DA -0.210 -0.369 0.327
summary(region_mdl)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: felicity_rating ~ cDA_presence * cContext_condition + region_country +
(1 + cDA_presence + cContext_condition | workerid) + (1 +
cDA_presence + cContext_condition | item_type)
Data: data_region
AIC BIC logLik deviance df.resid
238.3 337.3 -98.1 196.3 803
Scaled residuals:
Min 1Q Median 3Q Max
-3.2679 -0.5425 0.1101 0.5849 3.0926
Random effects:
Groups Name Variance Std.Dev. Corr
workerid (Intercept) 0.0223542 0.14951
cDA_presence 0.0093433 0.09666 -0.71
cContext_condition 0.0046385 0.06811 -0.35 -0.36
item_type (Intercept) 0.0029706 0.05450
cDA_presence 0.0007791 0.02791 0.33
cContext_condition 0.0007621 0.02761 -0.97 -0.10
Residual 0.0534671 0.23123
Number of obs: 824, groups: workerid, 103; item_type, 8
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 0.678572 0.040601 67.069214 16.713 < 2e-16 ***
cDA_presence 0.114982 0.016062 16.895281 7.159 1.66e-06 ***
cContext_condition 0.080439 0.014557 12.906910 5.526 0.0001 ***
region_countrymexico -0.038139 0.050308 102.491854 -0.758 0.4501
region_countryspain -0.070743 0.046598 98.609101 -1.518 0.1322
region_countryusa -0.079151 0.041890 96.141961 -1.890 0.0618 .
region_countryvenezuela -0.070523 0.052186 103.760639 -1.351 0.1795
cDA_presence:cContext_condition 0.018369 0.008563 675.287983 2.145 0.0323 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) cDA_pr cCntx_ rgn_cntrym rgn_cntrysp regn_cntrys rgn_cntryv
cDA_presenc -0.058
cCntxt_cndt -0.379 -0.140
rgn_cntrymx -0.585 0.001 0.019
rgn_cntrysp -0.629 0.003 0.012 0.507
regn_cntrys -0.697 -0.002 0.005 0.564 0.606
rgn_cntryvn -0.561 0.007 0.006 0.451 0.486 0.541
cDA_prsn:C_ -0.020 -0.026 -0.010 0.034 0.029 0.010 -0.005
---
title: "spanish_definite_articles"
output: html_notebook
---

# libraries

```{r}
library(tidyverse)
library(ggplot2)
library(dbplyr)
library(lmerTest)
library(languageR)
library(lme4)
library(wesanderson)
cbPalette <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "D55E00", "#CC79A7")
source("helpers.r")
```

# data filtering

```{r}
data = read.csv("main-trials.csv")
demo = read.csv("main-subject_information.csv")
```

```{r}
data <- data %>%
  left_join(demo, by="workerid")
```


```{r}
data = data %>%
  filter(attention_response != "NA" & item_type != "example3") %>%
  mutate(attention_pass = ifelse(attention_response == attention_correct_response, 1, 0)) %>%
  arrange(workerid)
```

```{r}
attention_exclusion = data %>%
  group_by(workerid) %>%
  summarize(accuracy = mean(attention_pass)) %>%
  mutate(exclude = ifelse(accuracy < 0.8, "yes", "no")) %>%
  filter(exclude == "yes")
```

```{r}
attention_correct = data %>%
  group_by(workerid) %>%
  summarize(accuracy = mean(attention_pass)) %>%
  mutate(exclude = ifelse(accuracy < 0.8, "yes", "no")) %>%
  filter(exclude == "no")
```

```{r}
nonnative = demo %>%
  filter(native != "yes")
```

experiment completion times before exclusions
```{r}
data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  ggplot(aes(x=time)) +
  geom_histogram()  +
  xlab("Average completion time") +
  ylab("Number of cases") +
  theme_bw()
```

Excluding from data frames
```{r}
data = data[!(data$workerid %in% attention_exclusion$workerid),]
data = data[!(data$workerid %in% nonnative$workerid),]

demo = demo[!(demo$workerid %in% attention_exclusion$workerid),]
demo = demo[!(demo$workerid %in% nonnative$workerid),]

```


Time exclusions after filtering data based on attention checks and native language
```{r}
time_exclusions = data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  filter(time < 10)

length(unique(time_exclusions$workerid))
```

experiment completion times before time exclusions
```{r}
data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  ggplot(aes(x=time)) +
  geom_histogram()  +
  xlab("Average completion time") +
  ylab("Number of cases") +
  theme_bw()
```

```{r}
data = data[!(data$workerid %in% time_exclusions$workerid),]
demo = demo[!(demo$workerid %in% time_exclusions$workerid),]
```


double check demo and data are the same
```{r}
length(demo$workerid)
length(unique(data$workerid))
```

```{r}
write.csv(demo, "demo.csv")
```


change character variable to factor variables
```{r}
data = data %>%
  mutate(age = as.numeric(age)) %>% 
  mutate_if(is.character,as.factor)

demo = demo %>%
  mutate(age = as.numeric(age))

demo = demo %>%
  mutate_if(is.character,as.factor)

```


# Main data visualizations

Set the background to be white instead of gray.
```{r}
theme_set(theme_bw())
```

experiment completion times
```{r}
data %>%
  group_by(workerid) %>%
  summarize(time = mean(Answer.time_in_minutes)) %>%
  ggplot(aes(x=time)) +
  geom_histogram()  +
  xlab("Average completion time") +
  ylab("Number of cases") +
  theme_bw()
```

Filtering data by critical and filler trials.
droplevels() gets rid of unused factor levels (i.e. "filler" in data_critical)
```{r}
data_critical = data %>%
  filter(trial_type == "critical") %>% 
  droplevels() 

data_filler = data %>% 
  filter(trial_type == "filler")
```


```{r felicity barplot}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=reorder(context_condition, mean_felicity), y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  scale_fill_manual(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  #geom_text(aes(label = mean_felicity), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))
```


```{r}
ggsave(file = "graphs/felicity_barplot.png",width=5,height=4)
```

```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(DA_presence, context_condition) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=DA_presence, y=mean_felicity, fill = context_condition)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
 #geom_text(aes(label = mean_felicity), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))
```


```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(DA_presence) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=DA_presence, y=mean_felicity)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  #scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
 geom_text(aes(label = mean_felicity), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))
```


```{r felicity box plot}
  data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
```


```{r affect bar plot}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=reorder(context_condition, mean_affect), y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=affect_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Mean Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  #scale_fill_manual(values = cbPalette) +
  scale_fill_manual(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
 #geom_text(aes(label = mean_affect), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))

```

```{r}
ggsave(file = "graphs/affect_barplot.png",width=5,height=4)
```

```{r  affect box plot}
  data_critical %>% 
  group_by(context_condition, DA_presence) %>%
  ggplot(aes(x = context_condition,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
```


```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=DA_presence, y=mean_affect)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #labs (x="Context Condition", y= "Mean Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  #scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
 geom_text(aes(label = mean_affect), size = 3) +
  theme(axis.text.x = element_text(vjust = 0.7))
```


```{r}
dodge = position_dodge(.9)

data %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7))
```

```{r}
dodge = position_dodge(.9)

data %>% 
  group_by(context_condition, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7))
```


```{r}
data %>% 
  group_by(condition) %>%
  summarize(mean_affect = mean(affect_rating)) %>%
  ggplot(aes(x=reorder(condition,mean_affect), y=mean_affect)) + 
  geom_bar(stat = "identity") +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.7))
```

```{r}
data_critical %>%
  ggplot(aes(x = affect_rating, y = felicity_rating, color = condition)) +
  geom_point() +
  geom_smooth(method="lm") +
  theme_bw()
```

```{r}
data %>%
  group_by(condition) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = condition)) +
  geom_point() +
  theme_bw()
```

```{r}
data_critical %>%
  group_by(context_condition, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition)) +
  geom_point(size =3) +
  theme_bw()
```



```{r}
data_critical %>%
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, group = DA_presence)) +
  geom_point(size =3) +
  geom_smooth(method = "lm") + 
  theme_bw()
```
## Variation by item types
```{r}
data_critical %>%
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, group = DA_presence, label = item_type)) +
  geom_point(size =3) +
  geom_text() +
  geom_smooth(method = "lm")
```

```{r}
  data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
  facet_wrap(~item_type)
```


```{r}
  data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  ggplot(aes(x = context_condition,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  facet_wrap(~item_type)
```

```{r}
  data_critical %>% 
  group_by(DA_presence, item_type) %>%
  ggplot(aes(x = item_type,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA"))
```

```{r}
  data_critical %>% 
  group_by(DA_presence, item_type) %>%
  ggplot(aes(x = item_type,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Affect Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA"))
```

Plot the felcity and affect ratings for each item_type by using facets.

```{r}
ggplot(data, aes(x=affect_rating,y=felicity_rating, color=condition)) +
  geom_point() +
  geom_smooth(method="lm") + 
  facet_wrap(~item_type)
```

Plot the felcity and affect ratings for each item_type by using facets.

```{r}
ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=condition)) +
  geom_point() +
  #geom_smooth(method="lm") + 
  geom_smooth() + 
  facet_wrap(~item_type)
```

```{r}
ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=context_condition)) +
  geom_point() +
  geom_smooth(method="lm") + 
  facet_wrap(~item_type)
```

```{r}
ggplot(data_filler, aes(x=affect_rating,y=felicity_rating, color=context_condition)) +
  geom_point() +
  geom_smooth(method="lm") + 
  facet_wrap(~item_type)
```

```{r Positive and Negative Filler plots}
data_filler %>% 
  filter(item_type == "cellphone" | item_type == "swimming") %>%  
  ggplot(aes(x=affect_rating,y=felicity_rating, color=context_condition)) +
  geom_point() +
  geom_smooth(method="lm") + 
  labs (x="Affect Rating", y= "Felicity Rating", color = "Context Condition") +
  scale_color_manual(labels=c("Negative Filler", "Positive Filler"), values = wes_palette("Darjeeling1")) +
  facet_wrap(~item_type)
```

```{r}
ggsave(file = "graphs/affect_fillers_scatterplot.png",width=8,height=4)
```

```{r DA presence by item type}
ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
  geom_point() +
  #geom_smooth(method="lm") + 
  geom_smooth() + 
  labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
  #scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  scale_color_manual(labels=c("DA", "no DA"), values = cbPalette) +
  facet_wrap(~item_type)
```
 
```{r}
ggsave(file = "graphs/DA_by_item_type.png",width=10,height=7)
```
 
```{r referent gender}
ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
  geom_point() +
  geom_smooth(method="lm") + 
  #geom_smooth() + 
  labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
  #scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  scale_color_manual(values = cbPalette) +
  facet_wrap(~referent_gender)
```
 
```{r}
ggplot(data_critical, aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
  geom_point() +
  geom_smooth(method="lm") + 
  #geom_smooth() + 
  labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
  #scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  scale_color_manual(values = cbPalette) +
  facet_grid(rows = vars(item_type), cols = vars(referent_gender))
``` 

```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(referent_gender, DA_presence, item_type) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=referent_gender, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~item_type)
```


```{r}
  data_critical %>% 
  group_by(DA_presence, referent_gender) %>%
  ggplot(aes(x = referent_gender,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  #labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
```

```{r}
  data_critical %>% 
  group_by(DA_presence, referent_gender) %>%
  ggplot(aes(x = referent_gender,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  #labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
```
 
```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~item_type)
```

```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence, item_type) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~item_type)
```


# Exploratory data visualizations for social variables

```{r}
data %>%
  group_by(region_country) %>%
  ggplot(aes(x = fct_infreq(region_country), fill=region_country)) +
  geom_bar(stat = 'count') +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.7)) +
  scale_fill_manual(values = wes_palette("Zissou1", 15, type = "continuous")) +
  theme(legend.position = "none")
```

```{r Participants by country}
demo %>%
  group_by(region_country) %>%
  ggplot(aes(x = fct_infreq(region_country), fill=region_country)) +
  geom_bar(stat = 'count') +
  theme(axis.text.x = element_text(angle = 45, vjust = 0.7)) +
  scale_fill_manual(values = wes_palette("Zissou1", 15, type = "continuous")) +
  theme(legend.position = "none") +
  labs (x="Country", y= "Number of Participants")
```

```{r}
ggsave(file = "graphs/participants_country.png",width=5,height=4)
```


```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence, region_country) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~region_country)
```

```{r}
data_critical %>%
  group_by(context_condition, DA_presence, region_country) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~region_country) +
  theme_bw()
```

```{r}
data_critical %>%
  group_by(context_condition, DA_presence, region_country) %>%
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>% 
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  #filter(trial_sum > 25) %>% 
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~region_country) +
  theme_bw()
```

```{r}
  data_critical %>% 
  group_by(context_condition, DA_presence, region_country) %>%
  ggplot(aes(x = context_condition,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "CG", "neutralNoCG" = "No CG")) +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  facet_wrap(~region_country)
```

```{r}
dodge = position_dodge(.9)

data_critical %>% 
  group_by(context_condition, DA_presence, region_country) %>%
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #scale_fill_manual(values = cbPalette) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~region_country)
```



```{r Felicity by top 5 countries}
  data_critical %>% 
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  group_by(DA_presence, region_country) %>%
  ggplot(aes(x = region_country,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Country", y= "Felicity Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA")) +
  scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
```

```{r}
ggsave(file = "graphs/country_felicity_boxplot.png",width=7,height=5)
```

```{r}
  data_critical %>% 
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  group_by(condition, region_country) %>%
  ggplot(aes(x = region_country,y=felicity_rating, fill = condition)) +
  geom_boxplot() +
  labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence")
  #scale_fill_discrete(labels=c("DA", "no DA"))
```

```{r}
  data_critical %>% 
  filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela") %>%
  group_by(DA_presence, region_country) %>%
  ggplot(aes(x = region_country,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  labs (x="Country", y= "Affect Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA")) +
 scale_fill_manual(values = wes_palette("FantasticFox1")[2:4])
```


```{r}
ggsave(file = "graphs/country_affect_boxplot.png",width=7,height=5)
```

```{r education}
data_critical %>%
  group_by(context_condition, DA_presence, education) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~education) +
  theme_bw()
```

```{r age}
data_critical %>%
  filter(!is.na(age)) %>% 
  group_by(workerid, age, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = age, y = mean_felicity, color = DA_presence)) +
  geom_point(size =3) +
  geom_smooth(method = "lm") +
  theme_bw()
```

```{r}
data_critical %>%
  filter(!is.na(age)) %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(workerid, age) %>%
  summarize(reported_usage = paste(unique(reported_usage))) %>%
  ggplot(aes(x = age, y = as.numeric(reported_usage))) +
  geom_point(size =3) +
  geom_smooth(method = "lm") +
  theme_bw()
```

```{r}
data_critical %>%
  filter(!is.na(age)) %>% 
  group_by(workerid, age, DA_presence) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating)) %>%
  ggplot(aes(x = age, y = mean_affect, color = DA_presence)) +
  geom_point(size =3) +
  geom_smooth(method = "lm") +
  geom_hline(yintercept = 0.5) +
  theme_bw()
```


```{r reported_usage}
data_critical %>%
  group_by(context_condition, DA_presence, reported_usage) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~reported_usage)
```

```{r}
  data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(DA_presence, reported_usage) %>%
  ggplot(aes(x = reported_usage,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  #labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA"))
```


```{r}
  data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3") %>%
  group_by(DA_presence, reported_usage) %>%
  ggplot(aes(x = reported_usage,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
  #labs (x="Item Type", y= "Affect Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA"))
```

```{r}
dodge = position_dodge(.9)

data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(condition, reported_usage) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x= reorder(reported_usage, mean_felicity), y=mean_felicity, fill = condition)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  #labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  #scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")) +
  theme_bw() +
  theme(axis.text.x = element_text(vjust = 0.7))
```

```{r}
dodge = position_dodge(.9)

data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(DA_presence, reported_usage, context_condition) %>%
  summarize(mean_felicity = mean(felicity_rating), CI.Low = ci.low(felicity_rating), CI.High = ci.high(felicity_rating)) %>%
  mutate(YMin = mean_felicity - CI.Low, YMax = mean_felicity + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_felicity, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Felicity Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")[2:4]) +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~ reported_usage)
```

```{r}
dodge = position_dodge(.9)

data_critical %>% 
  filter(reported_usage == "0" | reported_usage == "1" | reported_usage == "2" | reported_usage == "3" ) %>%
  group_by(DA_presence, reported_usage, context_condition) %>%
  summarize(mean_affect = mean(affect_rating), CI.Low = ci.low(affect_rating), CI.High = ci.high(affect_rating)) %>%
  mutate(YMin = mean_affect - CI.Low, YMax = mean_affect + CI.High) %>% 
  ggplot(aes(x=context_condition, y=mean_affect, fill = DA_presence)) + 
  geom_bar(stat = "identity", position = dodge) +
  #geom_jitter(data=data_critical,aes(y=felicity_rating),alpha=.5,color="lightblue") +
  geom_errorbar(aes(ymin = YMin, ymax = YMax), width = .25, position = dodge) +
  labs (x="Context Condition", y= "Affect Rating", fill = "DA Presence") +
  scale_x_discrete(labels=c("neutralCG" = "Common Ground", "neutralNoCG" = "No Common Ground")) +
  scale_fill_manual(values = cbPalette) +
  #scale_fill_manual(values = wes_palette("FantasticFox1")) +
  theme(axis.text.x = element_text(vjust = 0.7)) +
  facet_wrap(~ reported_usage)
```

```{r reported familiarity}
data_critical %>% 
  filter(reported_familiarity == "0" | reported_familiarity == "1" | reported_familiarity == "2" | reported_familiarity == "3" ) %>%
  group_by(DA_presence, reported_familiarity, context_condition) %>%
  ggplot(aes(x=affect_rating,y=felicity_rating, color=DA_presence)) +
  geom_point() +
  #geom_smooth(method="lm") + 
  geom_smooth() + 
  labs (x="Affect Rating", y= "Felicity Rating", color = "DA Presence") +
  #scale_color_discrete(labels=c("DA", "no DA"), values = wes_palette("FantasticFox1")[2:4]) +
  scale_color_manual(values = cbPalette) +
  facet_wrap(~reported_familiarity)
``` 

```{r}
data_critical %>%
  group_by(context_condition, DA_presence, reported_familiarity) %>%
  summarize(mean_affect = mean(affect_rating), mean_felicity = mean(felicity_rating), trial_sum = n()) %>%
  ggplot(aes(x = mean_affect, y = mean_felicity, color = DA_presence, shape = context_condition, size = trial_sum)) +
  geom_point() +
  facet_wrap(~reported_familiarity)
```

```{r}
  data_critical %>% 
  #filter(reported_familiarity == "0" | reported_familiarity == "1" | reported_familiarity == "2" | reported_familiarity == "3" ) %>%
  group_by(DA_presence, reported_familiarity) %>%
  ggplot(aes(x = reported_familiarity,y=felicity_rating, fill = DA_presence)) +
  geom_boxplot() +
  #labs (x="Item Type", y= "Felicity Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA"))
```



```{r}
  data_critical %>% 
  #filter(reported_familiarity == "0" | reported_familiarity == "1" | reported_familiarity == "2" | reported_familiarity == "3") %>%
  group_by(DA_presence, reported_familiarity) %>%
  ggplot(aes(x = reported_familiarity,y=affect_rating, fill = DA_presence)) +
  geom_boxplot() +
 #labs (x="Item Type", y= "Affect Rating", fill = "DA Presence") +
  scale_fill_discrete(labels=c("DA", "no DA"))
```

# Data modeling

Releveling (changing reference level) and centering DA predictor and context condition predictor
```{r}
data_model = data_critical %>% 
  mutate(context_condition = fct_relevel(context_condition, "neutralNoCG")) %>% 
  mutate(numDA_presence = as.numeric(DA_presence), numcontext_condition = as.numeric(context_condition)) %>% 
  mutate(cDA_presence = scale(numDA_presence), cContext_condition = scale(numcontext_condition))
```


1. (OSF preregistered analysis) Linear mixed effects analysis, predicting felicity from a centered presence of the DA predictor and a centered context condition predictor. By subject and by item, random intercepts as well as random slopes for condition and DA presence. 

```{r felicity model}
felicity_mdl <- lmer(felicity_rating ~ cDA_presence*cContext_condition + (1 + cDA_presence + cContext_condition|workerid) + (1 + cDA_presence + cContext_condition|item_type), data=data_model, REML=F)
summary(felicity_mdl)
```

Simple regressions to analyze interaction in felicity_mdl
```{r}
felicity_mdl.simple <- lmer(felicity_rating ~ DA_presence*context_condition - context_condition + (1 + DA_presence + context_condition|workerid) + (1 + DA_presence + context_condition|item_type), data=data_model, REML=F)
summary(felicity_mdl.simple)
```

```{r}
felicity_mdl.simple1.2 <- lmer(felicity_rating ~ DA_presence*context_condition - context_condition + (1 + DA_presence + context_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(felicity_mdl.simple1.2)
```


```{r}
felicity_mdl.simple2 <- lmer(felicity_rating ~ context_condition*DA_presence - DA_presence + (1 + DA_presence + context_condition|workerid) + (1 + DA_presence + context_condition|item_type), data=data_model, REML=F)
summary(felicity_mdl.simple2)
```


2. Linear mixed effects analysis, predicting affect from a centered presence of DA predictor and and a centered context condition predictor. By subject and by item, random intercepts as well as random slopes for condition and DA presence. 

```{r affect model}
affect_mdl <- lmer(affect_rating ~ cDA_presence*cContext_condition + (1 + cDA_presence + cContext_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl)
```

```{r}
affect_mdl.simple <- lmer(affect_rating ~ DA_presence*context_condition - context_condition + (1 + DA_presence + context_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl.simple)
```

```{r}
affect_mdl.simple2 <- lmer(affect_rating ~ context_condition*DA_presence - DA_presence + (1 + DA_presence + context_condition|workerid) + (1|item_type), data=data_model, REML=F)
summary(affect_mdl.simple2)
```

```{r}
data_region = data_model %>% 
filter(region_country == "argentina" | region_country == "mexico" | region_country == "spain" | region_country == "usa" | region_country == "venezuela")
```


```{r}
region_mdl <- lmer(felicity_rating ~ cDA_presence*cContext_condition + region_country + (1 + cDA_presence + cContext_condition|workerid) + (1 + cDA_presence + cContext_condition|item_type), data=data_region, REML=F)
summary(region_mdl)
```

